Understanding Consumer Preferences for Movie Trailers from EEG using Machine Learning
This work addresses the need for more accurate measures of consumer behavior in neuromarketing, though it is incremental as it extends prior EEG-based preference prediction to ordered choices.
The study tackled the problem of predicting ordered consumer preferences for movie trailers using EEG data, achieving a prediction accuracy of 72%, which is above chance.
Neuromarketing aims to understand consumer behavior using neuroscience. Brain imaging tools such as EEG have been used to better understand consumer behavior that goes beyond self-report measures which can be a more accurate measure to understand how and why consumers prefer choosing one product over another. Previous studies have shown that consumer preferences can be effectively predicted by understanding changes in evoked responses as captured by EEG. However, understanding ordered preference of choices was not studied earlier. In this study, we try to decipher the evoked responses using EEG while participants were presented with naturalistic stimuli i.e. movie trailers. Using Machine Learning tech niques to mine the patterns in EEG signals, we predicted the movie rating with more than above-chance, 72% accuracy. Our research shows that neural correlates can be an effective predictor of consumer choices and can significantly enhance our understanding of consumer behavior.